One of the major goals of the Infinity Project, is to help ourselves to define and pursue common goal: "if we are able to define our common goal, the problem of creating friendly artificial intelligence reduces to creating an optimization system to optimize for our common goal."

We started with a conceptualization of categories (need, goal, idea, plan, step, task, work), which people seem to use when they break down all of the work they do, and built a goal-pursuit/task-management system to help people define their goals explicitly.

Unfortunately, people don't always know what they want, and are often unable to explicitly define their true goals. Fortunately...

Probabilistic programming is a relatively new form of programming, that was developed as a result of a conceptual breakthrough in generalizing probabilistic modelling from inflexible Bayesian networks and graphical models to a probabilistic analogy of higher-order logic (HOL). There is a very good Google talk by Noah Goodman about this in the fourth conference on artificial general intelligence in 2011, explaining this new paradigm.

Unlike deep learning, probabilistic programming enables humans to understand the structure of complex probability distributions in a similar way that ...

The Infinity Project presents one possible way to understand how everything what people had ever created, was created, through introducing abstract categories (need, goal, idea, plan, step, task, work), which people seem to use when they break down the work they do. In this post, I take a look how the Infinity Project appears in the context of context-free grammars, - an abstract model for modelling languages. I conclude that from one stand point, these categories can be viewed as non-terminal symbols, based on which we could learn the production rules people use(d) to solve problems.

Every life form exists today, because it survived the evolutionary pressure over billions of years, which induced the inclination to choose actions optimizing for survival, so, life forms are good at recognizing what's good for me, but have difficulty in recognizing what's good universally. However, it seems there is a criterion to decide what is good universally -- good is to let everything exist, and bad is to destroy everything, where everything is defined as the world as a whole, as well as the world as its perspective from every no matter how small or large part of it.

Wouldn't it be cool to import Jupyter Notebook just as a Python module? Well, there is a convenient way, you just need to add one line to your Jupyter configuration to execute several Python functions. So, do:

OK. Python is not convenient for statistical data analysis, because you have to import a number of packages every time you want to do mathematical operations: NumPy, SciPy, Pandas, SkLearn, StatsModels, Sympy, etc. Follow this guide to create your own package of imports. I had started creating one, which suits me, -- the stt package, which you can use, after you install everything from http://ipython.org/.

用 Wine 安装 QQ2012 的做法 (Thanks, S!)

It is very convenient to collect and slice various data streams using MongoDB and Pandas (mentioned here). However, since I recently had been reading this wonderful Rob J Hyndman's R course on forecasting, I realized that I need to be able to be able to convert Pandas DataFrames to R's xts and ts objects.

While working with AdWords data in R, I wanted to get data from a Python function. In those situations, rPython package comes in handy. It lets you define and execute arbitrary Python function, and get the results.

1) Gave birth to life. Over four billion years ago, when first life forms appeard, they continued to exist, because certain molecules (like RNA) were able to process information, i.e., copy, and that was useful for survival;

2. 下定义你需要什么：

People who come from R to Python to do statistical analysis know that opening IPython does not immediately have the needed DataFrame class, and a number of shorthands for immediate analysis. So, here is a simple way to have them by default upon the start-up, which I find very useful.

MongoDB collections consists of binary JSON objects, the reading of which in Python is well covered here. However, I did not find a starightforward way to read the JSON objects into DataFrames, so here is one way I had found to complete the task.

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One of the greatest weak links for people's freedom is the connection of monetary system to life supplies: food supply, housing and transportation, and ultimately, energy supply. To free itself, the public has to automate, and own the means of automation of the supply of the goods needed for life.*, Mindey, 2010...

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